ArticleBMC genomics2020
Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 130 citations in OpenAlex.
- Sequence-based GWAS meta-analyses for beef production traits.Genetics, selection, evolution : GSE · 2023Pooled it
- Meta-analysis across Nellore cattle populations identifies common metabolic mechanisms that regulate feed efficiency-related traits.BMC genomics · 2022Pooled it
- Bayesian fine-mapping pinpoints candidate genes and pleiotropic loci of production traits from a chicken backcrossing scheme.BMC genomics · 2026Article
- CT scanning technology to asses the genetic and phenotypic correlations, and selection potential of carcass traits in mutton sheep.Animal bioscience · 2026Article
- Genome-wide association study of mature cow size traits in American Angus cattle.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Sequence-based genome-wide association study reveals genetic and metabolic mechanisms underlying feed efficiency-related traits in beef cattle.Journal of animal science and biotechnology · 2026Article
- Disentangling mature cow weight and body condition score: a comparative single-step GWAS of phenotypic pre-adjustment vs. recursive modeling.Journal of animal science · 2026Article
- Prediction accuracy for feed intake and body weight gain using host genomic and rumen metagenomic data in beef cattle.Genetics, selection, evolution : GSE · 2025Article
- Selective sweeps for mutations increasing height impede identification of causative mutations for fertility and other correlated traits in cattle.Genetics, selection, evolution : GSE · 2025Article
- Comprehensive Multi-omics Analysis of Regulatory Variants for Body Weight in Cattle.Genomics, proteomics & bioinformatics · 2025Article
- LCORL and STC2 Variants Increase Body Size and Growth Rate in Cattle and Other Animals.Genomics, proteomics & bioinformatics · 2025Article
- Genome-wide association studies and genetic architecture of carcass traits in Angus beef cattle using imputed whole-genome sequences data.Genetics, selection, evolution : GSE · 2025Article
- Characterizing differences in the muscle transcriptome between cattle with alternative LCORL-NCAPG haplotypes.BMC genomics · 2025Article
- Variations in rumen microbiota and host genome impacted feed efficiency in goat breeds.Frontiers in microbiology · 2025Article
- Article
- Weighted single step GWAS reveals genomic regions associated with economic traits in Murrah buffaloes.Animal biotechnology · 2024Article
- LCoRL Regulates Growth and Metabolism.Endocrinology · 2024Article
- Scans for Signatures of Selection in Genomes of Wagyu and Buryat Cattle Breeds Reveal Candidate Genes and Genetic Variants for Adaptive Phenotypes and Production Traits.Animals : an open access journal from MDPI · 2024Article
- Polymorphism rs2327430 in TCF21 predicts the risk and prognosis of gastric cancer by affecting the binding between TFAP2A and TCF21.Cancer cell international · 2024Article
- Exploring genomic inbreeding and selection signatures in a commercial Brangus herd through functional annotation.Journal of applied genetics · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundGenome wide association studies (GWAS) on residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) were conducted in a population of 7573 animals from multiple beef cattle breeds based on 7,853,211 imputed whole genome sequence variants. The GWAS results were used to elucidate genetic architectures of the feed efficiency related traits in beef cattle.
resultsThe DNA variant allele substitution effects approximated a bell-shaped distribution for all the traits while the distribution of additive genetic variances explained by single DNA variants followed a scaled inverse chi-squared distribution to a greater extent. With a threshold of P-value < 1.00E-05, 16, 72, 88, and 116 lead DNA variants on multiple chromosomes were significantly associated with RFI, DMI, ADG, and MWT, respectively. In addition, lead DNA variants with potentially large pleiotropic effects on DMI, ADG, and MWT were found on chromosomes 6, 14 and 20. On average, missense, 3'UTR, 5'UTR, and other regulatory region variants exhibited larger allele substitution effects in comparison to other functional classes. Intergenic and intron variants captured smaller proportions of additive genetic variance per DNA variant. Instead 3'UTR and synonymous variants explained a greater amount of genetic variance per DNA variant for all the traits examined while missense, 5'UTR and other regulatory region variants accounted for relatively more additive genetic variance per sequence variant for RFI and ADG, respectively. In total, 25 to 27 enriched cellular and molecular functions were identified with lipid metabolism and carbohydrate metabolism being the most significant for the feed efficiency traits.
conclusionsRFI is controlled by many DNA variants with relatively small effects whereas DMI, ADG, and MWT are influenced by a few DNA variants with large effects and many DNA variants with small effects. Nucleotide polymorphisms in regulatory region and synonymous functional classes play a more important role per sequence variant in determining variation of the feed efficiency traits. The genetic architecture as revealed by the GWAS of the imputed 7,853,211 DNA variants will improve our understanding on the genetic control of feed efficiency traits in beef cattle.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.